import torch.nn as nn

import math

import torch.utils.model_zoo as model_zoo



__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',

		   'resnet152']

model_urls = {

  'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',

  'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',

  'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',

  'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',

  'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',

}

def conv3x3(in_planes, out_planes, stride=1, dilation=1):

  """3x3 convolution with padding"""

  # original padding is 1; original dilation is 1

  return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,

				   padding=dilation, bias=False, dilation=dilation)

class BasicBlock(nn.Module):

  expansion = 1



  def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):

	super(BasicBlock, self).__init__()

	self.conv1 = conv3x3(inplanes, planes, stride, dilation)

	self.bn1 = nn.BatchNorm2d(planes)

	self.relu = nn.ReLU(inplace=True)

	self.conv2 = conv3x3(planes, planes)

	self.bn2 = nn.BatchNorm2d(planes)

	self.downsample = downsample

	self.stride = stride



  def forward(self, x):

	residual = x



	out = self.conv1(x)

	out = self.bn1(out)

	out = self.relu(out)



	out = self.conv2(out)

	out = self.bn2(out)



	if self.downsample is not None:

	  residual = self.downsample(x)



	out += residual

	out = self.relu(out)



	return out

class Bottleneck(nn.Module):

  expansion = 4



  def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):

	super(Bottleneck, self).__init__()

	self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)

	self.bn1 = nn.BatchNorm2d(planes)

	# original padding is 1; original dilation is 1

	self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation)

	self.bn2 = nn.BatchNorm2d(planes)

	self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)

	self.bn3 = nn.BatchNorm2d(planes * 4)

	self.relu = nn.ReLU(inplace=True)

	self.downsample = downsample

	self.stride = stride



  def forward(self, x):

	residual = x



	out = self.conv1(x)

	out = self.bn1(out)

	out = self.relu(out)



	out = self.conv2(out)

	out = self.bn2(out)

	out = self.relu(out)



	out = self.conv3(out)

	out = self.bn3(out)



	if self.downsample is not None:

	  residual = self.downsample(x)



	out += residual

	out = self.relu(out)



	return out

class ResNet(nn.Module):



  def __init__(self, block, layers, last_conv_stride=2, last_conv_dilation=1):



	self.inplanes = 64

	super(ResNet, self).__init__()

	self.conv1 = nn.Conv2d(9, 64, kernel_size=7, stride=2, padding=3,

						   bias=False)

	self.bn1 = nn.BatchNorm2d(64)

	self.relu = nn.ReLU(inplace=True)

	self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

	self.layer1 = self._make_layer(block, 64, layers[0])

	self.layer2 = self._make_layer(block, 128, layers[1], stride=2)

	self.layer3 = self._make_layer(block, 256, layers[2], stride=2)

	self.layer4 = self._make_layer(block, 512, layers[3], stride=last_conv_stride, dilation=last_conv_dilation)

	self.fc = nn.Linear(512*4*4, 512)
	self.fc_cls = nn.Linear(512, 74)

	for m in self.modules():

	  if isinstance(m, nn.Conv2d):

		n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

		m.weight.data.normal_(0, math.sqrt(2. / n))

	  elif isinstance(m, nn.BatchNorm2d):

		m.weight.data.fill_(1)

		m.bias.data.zero_()



  def _make_layer(self, block, planes, blocks, stride=1, dilation=1):

	downsample = None

	if stride != 1 or self.inplanes != planes * block.expansion:

	  downsample = nn.Sequential(

		nn.Conv2d(self.inplanes, planes * block.expansion,

				  kernel_size=1, stride=stride, bias=False),

		nn.BatchNorm2d(planes * block.expansion),

	  )



	layers = []

	layers.append(block(self.inplanes, planes, stride, downsample, dilation))

	self.inplanes = planes * block.expansion

	for i in range(1, blocks):

	  layers.append(block(self.inplanes, planes))



	return nn.Sequential(*layers)



  def forward(self, x):

  	batch, pose,frame = x.size()
	x = x.view(batch,3, pose/3,frame)
  	print(x.size())
	x = self.conv1(x)
	#print(x.size())
	x = self.bn1(x)

	x = self.relu(x)

	x = self.maxpool(x)



	x = self.layer1(x)

	x = self.layer2(x)

	x = self.layer3(x)

	x = self.layer4(x)

	#print(x.size())
	x = x.view(x.size(0), -1)
	fc = self.fc(x)
	out = self.fc_cls(fc)
	return fc, out

def remove_fc(state_dict):

  """Remove the fc layer parameters from state_dict."""

  for key, value in state_dict.items():

	if key.startswith('fc.'):

	  del state_dict[key]

  return state_dict

  
def resnet18(pretrained=False, **kwargs):

  """Constructs a ResNet-18 model.



  Args:

	  pretrained (bool): If True, returns a model pre-trained on ImageNet

  """

  model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)

  if pretrained:

	model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet18'])))

  return model

def resnet34(pretrained=False, **kwargs):

  """Constructs a ResNet-34 model.



  Args:

	  pretrained (bool): If True, returns a model pre-trained on ImageNet

  """

  model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)

  if pretrained:

	model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet34'])))

  return model

def resnet50(pretrained=False, **kwargs):

  """Constructs a ResNet-50 model.



  Args:

	  pretrained (bool): If True, returns a model pre-trained on ImageNet

  """

  model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)

  if pretrained:

	model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet50'])))

  return model

def resnet101(pretrained=False, **kwargs):

  """Constructs a ResNet-101 model.



  Args:

	  pretrained (bool): If True, returns a model pre-trained on ImageNet

  """

  model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)

  if pretrained:

	model.load_state_dict(

	  remove_fc(model_zoo.load_url(model_urls['resnet101'])))

  return model

def resnet152(pretrained=False, **kwargs):

  """Constructs a ResNet-152 model.



  Args:

	  pretrained (bool): If True, returns a model pre-trained on ImageNet

  """

  model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)

  if pretrained:

	model.load_state_dict(

	  remove_fc(model_zoo.load_url(model_urls['resnet152'])))

  return model